AI-Driven 2D Barcode Placement on 3D Contoured Surfaces
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Solution Overview
Problem
Current systems for optimizing 2D barcode placement in graphic design works are inefficient, particularly on 3D contoured surfaces, lacking objective feedback and integration with popular design tools, which hampers designers' ability to maximize scannability and effectiveness.
Innovation Solution
A system utilizing AI algorithms to analyze images and videos, providing optimal placement, size, and orientation suggestions for 2D barcodes, along with scoring and recommendations, integrated with design tools via APIs, to enhance visibility, scannability, and overall effectiveness, and offering testing and validation services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual analysis or trial-and-error approaches are used for barcode placement, then designers can place barcodes on various surfaces, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical analysis and trial-and-error placement methods with an automated computer-based system that uses image processing and algorithms to determine optimal barcode placement, thereby eliminating time-consuming manual operations
Solution Approach 2:
The system enables automated self-service barcode optimization by automatically analyzing images, evaluating placement options, and providing recommendations without requiring manual intervention, thus resolving the contradiction between ease of operation and time consumption
2Adaptability or versatility
If existing systems place 2D barcodes on 3D contoured surfaces, then barcodes can be applied to product packaging and billboards, but placement is suboptimal and scannability decreases
Solution Approach 1:
The patent applies local quality analysis by examining specific regions of 3D surfaces to determine optimal barcode placement locations that maintain scannability, evaluating local surface characteristics such as curvature, lighting, and texture to ensure reliable scanning while adapting to diverse surfaces
Solution Approach 2:
The system transitions from 2D barcode placement to 3D surface analysis by incorporating depth information and spatial characteristics, allowing barcodes to be placed on contoured surfaces while maintaining optimal scannability through multi-dimensional evaluation
3Ease of operation
If current methods are used for barcode placement, then designers can create designs, but there is no objective feedback or scoring to guide optimization
Solution Approach 1:
The patent implements feedback mechanisms by providing objective scoring systems that evaluate barcode placement quality based on multiple criteria including scannability, visibility, and aesthetic considerations, giving designers actionable information to optimize their designs
4Productivity
If existing systems operate independently, then barcode optimization can be performed, but integration with popular design tools is lacking
Solution Approach 1:
The patent achieves universality by enabling the barcode optimization system to integrate with multiple popular design tools through APIs, allowing the same optimization capabilities to be accessed across different design workflows and platforms
Data Source
AI summary
The present invention relates to a system and method for optimizing the placement, size, scannability, and effectiveness of optical labels, such as 2D barcodes or machine-readable labels like QR codes, on a specified medium. The system comprises a set of servers configured to execute an artificial intelligence (AI) algorithm, a set of user devices, and a database. The AI algorithm analyzes uploaded images of intended mediums for optical label placement, determines optimal placement, size, and orientation of the optical labels, and calculates individual scores for visibility, scannability, and likelihood of being noticed by potential users for each suggestion, then combines these into an overall readability score. The invention offers a user-friendly, efficient, and objective approach to optimizing optical label placement that is particularly effective for addressing considerations for physical object mediums.